Overview

Dataset statistics

Number of variables35
Number of observations67463
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.0 MiB
Average record size in memory280.0 B

Variable types

Numeric21
Categorical13
Boolean1

Alerts

Payment Plan has constant value "False"Constant
Accounts Delinquent has constant value "0"Constant
Loan Title has a high cardinality: 109 distinct valuesHigh cardinality
ID has unique valuesUnique
Delinquency - two years has 52054 (77.2%) zerosZeros
Inquires - six months has 60486 (89.7%) zerosZeros

Reproduction

Analysis started2022-12-01 15:53:35.372875
Analysis finished2022-12-01 15:55:22.347730
Duration1 minute and 46.97 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

ID
Real number (ℝ)

Distinct67463
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25627608
Minimum1297933
Maximum72245779
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size527.2 KiB
2022-12-01T15:55:22.491929image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1297933
5-th percentile2281587.4
Q16570288
median17915646
Q342715208
95-th percentile63732607
Maximum72245779
Range70947846
Interquartile range (IQR)36144920

Descriptive statistics

Standard deviation21091554
Coefficient of variation (CV)0.82300128
Kurtosis-1.0899404
Mean25627608
Median Absolute Deviation (MAD)14302283
Skewness0.55657739
Sum1.7289153 × 1012
Variance4.4485365 × 1014
MonotonicityNot monotonic
2022-12-01T15:55:22.635585image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65087372 1
 
< 0.1%
67169611 1
 
< 0.1%
51765039 1
 
< 0.1%
31003776 1
 
< 0.1%
7030387 1
 
< 0.1%
24541254 1
 
< 0.1%
64224763 1
 
< 0.1%
10506056 1
 
< 0.1%
4004703 1
 
< 0.1%
18227383 1
 
< 0.1%
Other values (67453) 67453
> 99.9%
ValueCountFrequency (%)
1297933 1
< 0.1%
1298156 1
< 0.1%
1298576 1
< 0.1%
1298988 1
< 0.1%
1299125 1
< 0.1%
1299130 1
< 0.1%
1300238 1
< 0.1%
1300577 1
< 0.1%
1300838 1
< 0.1%
1301081 1
< 0.1%
ValueCountFrequency (%)
72245779 1
< 0.1%
72191501 1
< 0.1%
72187231 1
< 0.1%
72182515 1
< 0.1%
72134965 1
< 0.1%
72113342 1
< 0.1%
72101854 1
< 0.1%
72076506 1
< 0.1%
72065755 1
< 0.1%
72050446 1
< 0.1%

Loan Amount
Real number (ℝ)

Distinct27525
Distinct (%)40.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16848.903
Minimum1014
Maximum35000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size527.2 KiB
2022-12-01T15:55:22.786144image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1014
5-th percentile4485.1
Q110012
median16073
Q322106
95-th percentile31741
Maximum35000
Range33986
Interquartile range (IQR)12094

Descriptive statistics

Standard deviation8367.8657
Coefficient of variation (CV)0.49664158
Kurtosis-0.79813668
Mean16848.903
Median Absolute Deviation (MAD)6048
Skewness0.28808301
Sum1.1366775 × 109
Variance70021177
MonotonicityNot monotonic
2022-12-01T15:55:22.936686image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15932 13
 
< 0.1%
14424 12
 
< 0.1%
15800 11
 
< 0.1%
15639 11
 
< 0.1%
15118 11
 
< 0.1%
14556 11
 
< 0.1%
14689 11
 
< 0.1%
15962 11
 
< 0.1%
15348 11
 
< 0.1%
15811 10
 
< 0.1%
Other values (27515) 67351
99.8%
ValueCountFrequency (%)
1014 1
< 0.1%
1020 1
< 0.1%
1024 1
< 0.1%
1025 1
< 0.1%
1030 1
< 0.1%
1031 1
< 0.1%
1036 1
< 0.1%
1038 1
< 0.1%
1045 1
< 0.1%
1046 1
< 0.1%
ValueCountFrequency (%)
35000 1
< 0.1%
34999 1
< 0.1%
34997 1
< 0.1%
34996 1
< 0.1%
34995 1
< 0.1%
34993 1
< 0.1%
34991 1
< 0.1%
34990 1
< 0.1%
34988 2
< 0.1%
34987 1
< 0.1%

Funded Amount
Real number (ℝ)

Distinct24548
Distinct (%)36.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15770.599
Minimum1014
Maximum34999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size527.2 KiB
2022-12-01T15:55:23.095302image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1014
5-th percentile5894.3
Q19266.5
median13042
Q321793
95-th percentile32112.8
Maximum34999
Range33985
Interquartile range (IQR)12526.5

Descriptive statistics

Standard deviation8150.9927
Coefficient of variation (CV)0.51684737
Kurtosis-0.61713242
Mean15770.599
Median Absolute Deviation (MAD)5097
Skewness0.67263298
Sum1.0639319 × 109
Variance66438681
MonotonicityNot monotonic
2022-12-01T15:55:23.229941image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10835 16
 
< 0.1%
11034 15
 
< 0.1%
11451 14
 
< 0.1%
10728 14
 
< 0.1%
7691 14
 
< 0.1%
11187 14
 
< 0.1%
8860 13
 
< 0.1%
11080 13
 
< 0.1%
8795 13
 
< 0.1%
10946 13
 
< 0.1%
Other values (24538) 67324
99.8%
ValueCountFrequency (%)
1014 1
< 0.1%
1032 1
< 0.1%
1080 1
< 0.1%
1087 1
< 0.1%
1098 1
< 0.1%
1153 1
< 0.1%
1154 1
< 0.1%
1163 1
< 0.1%
1179 1
< 0.1%
1236 1
< 0.1%
ValueCountFrequency (%)
34999 2
< 0.1%
34998 2
< 0.1%
34995 1
< 0.1%
34994 1
< 0.1%
34993 1
< 0.1%
34988 1
< 0.1%
34986 2
< 0.1%
34983 1
< 0.1%
34982 2
< 0.1%
34977 1
< 0.1%

Funded Amount Investor
Real number (ℝ)

Distinct67441
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14621.799
Minimum1114.5902
Maximum34999.746
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size527.2 KiB
2022-12-01T15:55:23.386305image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1114.5902
5-th percentile6327.9798
Q19831.685
median12793.682
Q317807.594
95-th percentile28884.736
Maximum34999.746
Range33885.156
Interquartile range (IQR)7975.9091

Descriptive statistics

Standard deviation6785.3452
Coefficient of variation (CV)0.46405678
Kurtosis0.46186792
Mean14621.799
Median Absolute Deviation (MAD)3564.6612
Skewness0.99013878
Sum9.8643045 × 108
Variance46040909
MonotonicityNot monotonic
2022-12-01T15:55:23.537350image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12099.7183 2
 
< 0.1%
7890.447955 2
 
< 0.1%
13910.43024 2
 
< 0.1%
8879.914835 2
 
< 0.1%
12367.56806 2
 
< 0.1%
31818.45799 2
 
< 0.1%
10417.21663 2
 
< 0.1%
14861.31507 2
 
< 0.1%
14238.25035 2
 
< 0.1%
10936.53653 2
 
< 0.1%
Other values (67431) 67443
> 99.9%
ValueCountFrequency (%)
1114.590204 1
< 0.1%
1127.754818 1
< 0.1%
1129.708853 1
< 0.1%
1242.527961 1
< 0.1%
1246.547591 1
< 0.1%
1250.787941 1
< 0.1%
1372.686804 1
< 0.1%
1441.583282 1
< 0.1%
1525.567016 1
< 0.1%
1537.528946 1
< 0.1%
ValueCountFrequency (%)
34999.74643 1
< 0.1%
34999.43383 1
< 0.1%
34997.89175 1
< 0.1%
34996.88747 1
< 0.1%
34995.26246 1
< 0.1%
34993.60145 1
< 0.1%
34993.49979 1
< 0.1%
34990.5952 1
< 0.1%
34988.98401 1
< 0.1%
34987.513 1
< 0.1%

Term
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size527.2 KiB
59
43780 
58
22226 
36
 
1457

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters134926
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row59
2nd row59
3rd row59
4th row59
5th row59

Common Values

ValueCountFrequency (%)
59 43780
64.9%
58 22226
32.9%
36 1457
 
2.2%

Length

2022-12-01T15:55:23.691145image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-01T15:55:23.824787image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
59 43780
64.9%
58 22226
32.9%
36 1457
 
2.2%

Most occurring characters

ValueCountFrequency (%)
5 66006
48.9%
9 43780
32.4%
8 22226
 
16.5%
3 1457
 
1.1%
6 1457
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 134926
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 66006
48.9%
9 43780
32.4%
8 22226
 
16.5%
3 1457
 
1.1%
6 1457
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 134926
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 66006
48.9%
9 43780
32.4%
8 22226
 
16.5%
3 1457
 
1.1%
6 1457
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 134926
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 66006
48.9%
9 43780
32.4%
8 22226
 
16.5%
3 1457
 
1.1%
6 1457
 
1.1%

Batch Enrolled
Categorical

Distinct41
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size527.2 KiB
BAT3873588
 
3626
BAT1586599
 
3142
BAT1104812
 
2996
BAT2252229
 
2557
BAT2803411
 
2425
Other values (36)
52717 

Length

Max length10
Median length10
Mean length9.9867335
Min length9

Characters and Unicode

Total characters673735
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBAT2522922
2nd rowBAT1586599
3rd rowBAT2136391
4th rowBAT2428731
5th rowBAT5341619

Common Values

ValueCountFrequency (%)
BAT3873588 3626
 
5.4%
BAT1586599 3142
 
4.7%
BAT1104812 2996
 
4.4%
BAT2252229 2557
 
3.8%
BAT2803411 2425
 
3.6%
BAT1780517 2403
 
3.6%
BAT1184694 2298
 
3.4%
BAT2078974 2290
 
3.4%
BAT2575549 2257
 
3.3%
BAT4694572 2248
 
3.3%
Other values (31) 41221
61.1%

Length

2022-12-01T15:55:23.928802image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bat3873588 3626
 
5.4%
bat1586599 3142
 
4.7%
bat1104812 2996
 
4.4%
bat2252229 2557
 
3.8%
bat2803411 2425
 
3.6%
bat1780517 2403
 
3.6%
bat1184694 2298
 
3.4%
bat2078974 2290
 
3.4%
bat2575549 2257
 
3.3%
bat4694572 2248
 
3.3%
Other values (31) 41221
61.1%

Most occurring characters

ValueCountFrequency (%)
2 69110
10.3%
B 67463
10.0%
A 67463
10.0%
T 67463
10.0%
1 65775
9.8%
5 54451
8.1%
4 54301
8.1%
8 46840
7.0%
3 45437
6.7%
9 40700
6.0%
Other values (3) 94732
14.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 471346
70.0%
Uppercase Letter 202389
30.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 69110
14.7%
1 65775
14.0%
5 54451
11.6%
4 54301
11.5%
8 46840
9.9%
3 45437
9.6%
9 40700
8.6%
6 38900
8.3%
7 36194
7.7%
0 19638
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
B 67463
33.3%
A 67463
33.3%
T 67463
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 471346
70.0%
Latin 202389
30.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 69110
14.7%
1 65775
14.0%
5 54451
11.6%
4 54301
11.5%
8 46840
9.9%
3 45437
9.6%
9 40700
8.6%
6 38900
8.3%
7 36194
7.7%
0 19638
 
4.2%
Latin
ValueCountFrequency (%)
B 67463
33.3%
A 67463
33.3%
T 67463
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 673735
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 69110
10.3%
B 67463
10.0%
A 67463
10.0%
T 67463
10.0%
1 65775
9.8%
5 54451
8.1%
4 54301
8.1%
8 46840
7.0%
3 45437
6.7%
9 40700
6.0%
Other values (3) 94732
14.1%

Interest Rate
Real number (ℝ)

Distinct67448
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.846258
Minimum5.3200058
Maximum27.182348
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size527.2 KiB
2022-12-01T15:55:24.049517image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum5.3200058
5-th percentile6.1348234
Q19.2971472
median11.377696
Q314.193533
95-th percentile18.600114
Maximum27.182348
Range21.862342
Interquartile range (IQR)4.8963859

Descriptive statistics

Standard deviation3.7186287
Coefficient of variation (CV)0.31390746
Kurtosis0.1490136
Mean11.846258
Median Absolute Deviation (MAD)2.3790711
Skewness0.56338284
Sum799184.1
Variance13.8282
MonotonicityNot monotonic
2022-12-01T15:55:24.176273image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.191126068 2
 
< 0.1%
8.637499241 2
 
< 0.1%
9.996610601 2
 
< 0.1%
9.53034331 2
 
< 0.1%
11.97836961 2
 
< 0.1%
9.28661329 2
 
< 0.1%
9.031164731 2
 
< 0.1%
11.2653123 2
 
< 0.1%
17.97529763 2
 
< 0.1%
15.78960848 2
 
< 0.1%
Other values (67438) 67443
> 99.9%
ValueCountFrequency (%)
5.320005799 1
< 0.1%
5.320159165 1
< 0.1%
5.320433439 1
< 0.1%
5.320547017 1
< 0.1%
5.321130759 1
< 0.1%
5.321256189 1
< 0.1%
5.322212834 1
< 0.1%
5.322458098 1
< 0.1%
5.322651425 1
< 0.1%
5.322937103 1
< 0.1%
ValueCountFrequency (%)
27.18234758 1
< 0.1%
27.07000405 1
< 0.1%
27.01820329 1
< 0.1%
26.9329474 1
< 0.1%
26.92044891 1
< 0.1%
26.83306079 1
< 0.1%
26.54588757 1
< 0.1%
26.51588192 1
< 0.1%
26.32641724 1
< 0.1%
26.31597117 1
< 0.1%

Grade
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size527.2 KiB
C
19085 
B
18742 
A
12055 
D
8259 
E
6446 
Other values (2)
2876 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters67463
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowC
3rd rowF
4th rowC
5th rowC

Common Values

ValueCountFrequency (%)
C 19085
28.3%
B 18742
27.8%
A 12055
17.9%
D 8259
12.2%
E 6446
 
9.6%
F 2246
 
3.3%
G 630
 
0.9%

Length

2022-12-01T15:55:24.323884image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-01T15:55:24.452224image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
c 19085
28.3%
b 18742
27.8%
a 12055
17.9%
d 8259
12.2%
e 6446
 
9.6%
f 2246
 
3.3%
g 630
 
0.9%

Most occurring characters

ValueCountFrequency (%)
C 19085
28.3%
B 18742
27.8%
A 12055
17.9%
D 8259
12.2%
E 6446
 
9.6%
F 2246
 
3.3%
G 630
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 67463
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 19085
28.3%
B 18742
27.8%
A 12055
17.9%
D 8259
12.2%
E 6446
 
9.6%
F 2246
 
3.3%
G 630
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 67463
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 19085
28.3%
B 18742
27.8%
A 12055
17.9%
D 8259
12.2%
E 6446
 
9.6%
F 2246
 
3.3%
G 630
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 67463
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 19085
28.3%
B 18742
27.8%
A 12055
17.9%
D 8259
12.2%
E 6446
 
9.6%
F 2246
 
3.3%
G 630
 
0.9%

Sub Grade
Categorical

Distinct35
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size527.2 KiB
B4
 
4462
C1
 
4188
B3
 
3999
A5
 
3540
B2
 
3520
Other values (30)
47754 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters134926
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC4
2nd rowD3
3rd rowD4
4th rowC3
5th rowD4

Common Values

ValueCountFrequency (%)
B4 4462
 
6.6%
C1 4188
 
6.2%
B3 3999
 
5.9%
A5 3540
 
5.2%
B2 3520
 
5.2%
B5 3408
 
5.1%
D1 3304
 
4.9%
C4 3250
 
4.8%
C2 3219
 
4.8%
C3 3121
 
4.6%
Other values (25) 31452
46.6%

Length

2022-12-01T15:55:24.569283image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b4 4462
 
6.6%
c1 4188
 
6.2%
b3 3999
 
5.9%
a5 3540
 
5.2%
b2 3520
 
5.2%
b5 3408
 
5.1%
d1 3304
 
4.9%
c4 3250
 
4.8%
c2 3219
 
4.8%
c3 3121
 
4.6%
Other values (25) 31452
46.6%

Most occurring characters

ValueCountFrequency (%)
B 18313
13.6%
C 16250
12.0%
1 14268
10.6%
4 13735
10.2%
2 13679
10.1%
5 13007
9.6%
3 12774
9.5%
D 11093
8.2%
A 10690
7.9%
E 6251
 
4.6%
Other values (2) 4866
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 67463
50.0%
Decimal Number 67463
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 18313
27.1%
C 16250
24.1%
D 11093
16.4%
A 10690
15.8%
E 6251
 
9.3%
F 3372
 
5.0%
G 1494
 
2.2%
Decimal Number
ValueCountFrequency (%)
1 14268
21.1%
4 13735
20.4%
2 13679
20.3%
5 13007
19.3%
3 12774
18.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 67463
50.0%
Common 67463
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 18313
27.1%
C 16250
24.1%
D 11093
16.4%
A 10690
15.8%
E 6251
 
9.3%
F 3372
 
5.0%
G 1494
 
2.2%
Common
ValueCountFrequency (%)
1 14268
21.1%
4 13735
20.4%
2 13679
20.3%
5 13007
19.3%
3 12774
18.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 134926
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 18313
13.6%
C 16250
12.0%
1 14268
10.6%
4 13735
10.2%
2 13679
10.1%
5 13007
9.6%
3 12774
9.5%
D 11093
8.2%
A 10690
7.9%
E 6251
 
4.6%
Other values (2) 4866
 
3.6%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size527.2 KiB
MORTGAGE
36351 
RENT
24150 
OWN
6962 

Length

Max length8
Median length8
Mean length6.0521175
Min length3

Characters and Unicode

Total characters408294
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMORTGAGE
2nd rowRENT
3rd rowMORTGAGE
4th rowMORTGAGE
5th rowMORTGAGE

Common Values

ValueCountFrequency (%)
MORTGAGE 36351
53.9%
RENT 24150
35.8%
OWN 6962
 
10.3%

Length

2022-12-01T15:55:24.667576image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-01T15:55:24.770300image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
mortgage 36351
53.9%
rent 24150
35.8%
own 6962
 
10.3%

Most occurring characters

ValueCountFrequency (%)
G 72702
17.8%
R 60501
14.8%
T 60501
14.8%
E 60501
14.8%
O 43313
10.6%
M 36351
8.9%
A 36351
8.9%
N 31112
7.6%
W 6962
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 408294
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 72702
17.8%
R 60501
14.8%
T 60501
14.8%
E 60501
14.8%
O 43313
10.6%
M 36351
8.9%
A 36351
8.9%
N 31112
7.6%
W 6962
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 408294
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 72702
17.8%
R 60501
14.8%
T 60501
14.8%
E 60501
14.8%
O 43313
10.6%
M 36351
8.9%
A 36351
8.9%
N 31112
7.6%
W 6962
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 408294
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G 72702
17.8%
R 60501
14.8%
T 60501
14.8%
E 60501
14.8%
O 43313
10.6%
M 36351
8.9%
A 36351
8.9%
N 31112
7.6%
W 6962
 
1.7%

Home Ownership
Real number (ℝ)

Distinct67454
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80541.503
Minimum14573.537
Maximum406561.54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size527.2 KiB
2022-12-01T15:55:24.892260image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum14573.537
5-th percentile33448.726
Q151689.843
median69335.833
Q394623.323
95-th percentile168296.72
Maximum406561.54
Range391988
Interquartile range (IQR)42933.479

Descriptive statistics

Standard deviation45029.12
Coefficient of variation (CV)0.55907972
Kurtosis7.0277345
Mean80541.503
Median Absolute Deviation (MAD)20118.159
Skewness2.1304881
Sum5.4335714 × 109
Variance2.0276217 × 109
MonotonicityNot monotonic
2022-12-01T15:55:25.031885image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39753.81982 2
 
< 0.1%
71159.7124 2
 
< 0.1%
61831.12988 2
 
< 0.1%
27139.67231 2
 
< 0.1%
35858.04083 2
 
< 0.1%
91745.81071 2
 
< 0.1%
37623.24185 2
 
< 0.1%
28714.13609 2
 
< 0.1%
58867.57595 2
 
< 0.1%
30224.43666 1
 
< 0.1%
Other values (67444) 67444
> 99.9%
ValueCountFrequency (%)
14573.53717 1
< 0.1%
14652.37968 1
< 0.1%
14678.63863 1
< 0.1%
14788.61394 1
< 0.1%
14836.55226 1
< 0.1%
14859.64954 1
< 0.1%
14901.41773 1
< 0.1%
14938.0786 1
< 0.1%
14996.99281 1
< 0.1%
15013.52595 1
< 0.1%
ValueCountFrequency (%)
406561.5364 1
< 0.1%
405697.0616 1
< 0.1%
404550.444 1
< 0.1%
401352.3764 1
< 0.1%
400877.5635 1
< 0.1%
400676.3457 1
< 0.1%
400348.8196 1
< 0.1%
399925.7864 1
< 0.1%
399103.7444 1
< 0.1%
398416.3107 1
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size527.2 KiB
Source Verified
33036 
Verified
18078 
Not Verified
16349 

Length

Max length15
Median length12
Mean length12.397195
Min length8

Characters and Unicode

Total characters836352
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Verified
2nd rowSource Verified
3rd rowSource Verified
4th rowSource Verified
5th rowSource Verified

Common Values

ValueCountFrequency (%)
Source Verified 33036
49.0%
Verified 18078
26.8%
Not Verified 16349
24.2%

Length

2022-12-01T15:55:25.163542image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-01T15:55:25.269259image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
verified 67463
57.7%
source 33036
28.3%
not 16349
 
14.0%

Most occurring characters

ValueCountFrequency (%)
e 167962
20.1%
i 134926
16.1%
r 100499
12.0%
V 67463
8.1%
f 67463
8.1%
d 67463
8.1%
o 49385
 
5.9%
49385
 
5.9%
S 33036
 
4.0%
u 33036
 
4.0%
Other values (3) 65734
 
7.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 670119
80.1%
Uppercase Letter 116848
 
14.0%
Space Separator 49385
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 167962
25.1%
i 134926
20.1%
r 100499
15.0%
f 67463
10.1%
d 67463
10.1%
o 49385
 
7.4%
u 33036
 
4.9%
c 33036
 
4.9%
t 16349
 
2.4%
Uppercase Letter
ValueCountFrequency (%)
V 67463
57.7%
S 33036
28.3%
N 16349
 
14.0%
Space Separator
ValueCountFrequency (%)
49385
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 786967
94.1%
Common 49385
 
5.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 167962
21.3%
i 134926
17.1%
r 100499
12.8%
V 67463
8.6%
f 67463
8.6%
d 67463
8.6%
o 49385
 
6.3%
S 33036
 
4.2%
u 33036
 
4.2%
c 33036
 
4.2%
Other values (2) 32698
 
4.2%
Common
ValueCountFrequency (%)
49385
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 836352
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 167962
20.1%
i 134926
16.1%
r 100499
12.0%
V 67463
8.1%
f 67463
8.1%
d 67463
8.1%
o 49385
 
5.9%
49385
 
5.9%
S 33036
 
4.0%
u 33036
 
4.0%
Other values (3) 65734
 
7.9%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size66.0 KiB
False
67463 
ValueCountFrequency (%)
False 67463
100.0%
2022-12-01T15:55:25.368992image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Loan Title
Categorical

Distinct109
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size527.2 KiB
Credit card refinancing
30728 
Debt consolidation
24841 
Debt Consolidation
3544 
Other
 
2455
Home improvement
 
2211
Other values (104)
3684 

Length

Max length26
Median length25
Mean length19.487778
Min length2

Characters and Unicode

Total characters1314704
Distinct characters49
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDebt Consolidation
2nd rowDebt consolidation
3rd rowDebt Consolidation
4th rowDebt consolidation
5th rowCredit card refinancing

Common Values

ValueCountFrequency (%)
Credit card refinancing 30728
45.5%
Debt consolidation 24841
36.8%
Debt Consolidation 3544
 
5.3%
Other 2455
 
3.6%
Home improvement 2211
 
3.3%
Major purchase 487
 
0.7%
Medical expenses 237
 
0.4%
Business 183
 
0.3%
Moving and relocation 157
 
0.2%
Car financing 135
 
0.2%
Other values (99) 2485
 
3.7%

Length

2022-12-01T15:55:25.475708image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
credit 31237
19.2%
card 31157
19.1%
refinancing 30728
18.9%
consolidation 29084
17.9%
debt 28860
17.7%
other 2455
 
1.5%
home 2420
 
1.5%
improvement 2307
 
1.4%
loan 567
 
0.3%
major 487
 
0.3%
Other values (48) 3524
 
2.2%

Most occurring characters

ValueCountFrequency (%)
n 155395
11.8%
i 154663
11.8%
e 103084
7.8%
r 99573
7.6%
95363
 
7.3%
t 94321
 
7.2%
a 94316
 
7.2%
o 94256
 
7.2%
d 92331
 
7.0%
c 88149
 
6.7%
Other values (39) 243253
18.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1146402
87.2%
Space Separator 95363
 
7.3%
Uppercase Letter 72868
 
5.5%
Decimal Number 58
 
< 0.1%
Dash Punctuation 13
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 155395
13.6%
i 154663
13.5%
e 103084
9.0%
r 99573
8.7%
t 94321
8.2%
a 94316
8.2%
o 94256
8.2%
d 92331
8.1%
c 88149
7.7%
f 31602
 
2.8%
Other values (13) 138712
12.1%
Uppercase Letter
ValueCountFrequency (%)
C 35943
49.3%
D 28757
39.5%
O 2668
 
3.7%
H 2421
 
3.3%
M 941
 
1.3%
L 537
 
0.7%
B 270
 
0.4%
P 251
 
0.3%
I 200
 
0.3%
R 155
 
0.2%
Other values (10) 725
 
1.0%
Decimal Number
ValueCountFrequency (%)
1 40
69.0%
2 6
 
10.3%
0 6
 
10.3%
3 6
 
10.3%
Space Separator
ValueCountFrequency (%)
95363
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1219270
92.7%
Common 95434
 
7.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 155395
12.7%
i 154663
12.7%
e 103084
8.5%
r 99573
8.2%
t 94321
7.7%
a 94316
7.7%
o 94256
7.7%
d 92331
7.6%
c 88149
7.2%
C 35943
 
2.9%
Other values (33) 207239
17.0%
Common
ValueCountFrequency (%)
95363
99.9%
1 40
 
< 0.1%
- 13
 
< 0.1%
2 6
 
< 0.1%
0 6
 
< 0.1%
3 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1314704
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 155395
11.8%
i 154663
11.8%
e 103084
7.8%
r 99573
7.6%
95363
 
7.3%
t 94321
 
7.2%
a 94316
 
7.2%
o 94256
 
7.2%
d 92331
 
7.0%
c 88149
 
6.7%
Other values (39) 243253
18.5%

Debit to Income
Real number (ℝ)

Distinct67454
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.299241
Minimum0.67529909
Maximum39.629862
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size527.2 KiB
2022-12-01T15:55:25.613340image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.67529909
5-th percentile10.205682
Q116.756416
median22.656658
Q330.0484
95-th percentile37.396621
Maximum39.629862
Range38.954563
Interquartile range (IQR)13.291983

Descriptive statistics

Standard deviation8.4518237
Coefficient of variation (CV)0.36275104
Kurtosis-0.90502119
Mean23.299241
Median Absolute Deviation (MAD)6.5435829
Skewness0.080966764
Sum1571836.7
Variance71.433324
MonotonicityNot monotonic
2022-12-01T15:55:25.771057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.97736061 2
 
< 0.1%
22.36852703 2
 
< 0.1%
18.79251904 2
 
< 0.1%
35.46709898 2
 
< 0.1%
24.50545264 2
 
< 0.1%
17.62506899 2
 
< 0.1%
27.34419347 2
 
< 0.1%
38.97468269 2
 
< 0.1%
24.41063595 2
 
< 0.1%
37.12728123 1
 
< 0.1%
Other values (67444) 67444
> 99.9%
ValueCountFrequency (%)
0.675299086 1
< 0.1%
0.763630198 1
< 0.1%
0.961457001 1
< 0.1%
1.117458713 1
< 0.1%
1.237228585 1
< 0.1%
1.300557774 1
< 0.1%
1.329297599 1
< 0.1%
1.372873946 1
< 0.1%
1.391419093 1
< 0.1%
1.397374381 1
< 0.1%
ValueCountFrequency (%)
39.62986189 1
< 0.1%
39.62964383 1
< 0.1%
39.6278639 1
< 0.1%
39.62757603 1
< 0.1%
39.62741588 1
< 0.1%
39.62732201 1
< 0.1%
39.62731 1
< 0.1%
39.62664966 1
< 0.1%
39.62590781 1
< 0.1%
39.62567144 1
< 0.1%

Delinquency - two years
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.32712746
Minimum0
Maximum8
Zeros52054
Zeros (%)77.2%
Negative0
Negative (%)0.0%
Memory size527.2 KiB
2022-12-01T15:55:25.907737image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.80088838
Coefficient of variation (CV)2.4482456
Kurtosis30.676297
Mean0.32712746
Median Absolute Deviation (MAD)0
Skewness4.6350213
Sum22069
Variance0.64142219
MonotonicityNot monotonic
2022-12-01T15:55:26.008790image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 52054
77.2%
1 11736
 
17.4%
2 2651
 
3.9%
3 445
 
0.7%
7 252
 
0.4%
6 191
 
0.3%
5 74
 
0.1%
8 44
 
0.1%
4 16
 
< 0.1%
ValueCountFrequency (%)
0 52054
77.2%
1 11736
 
17.4%
2 2651
 
3.9%
3 445
 
0.7%
4 16
 
< 0.1%
5 74
 
0.1%
6 191
 
0.3%
7 252
 
0.4%
8 44
 
0.1%
ValueCountFrequency (%)
8 44
 
0.1%
7 252
 
0.4%
6 191
 
0.3%
5 74
 
0.1%
4 16
 
< 0.1%
3 445
 
0.7%
2 2651
 
3.9%
1 11736
 
17.4%
0 52054
77.2%

Inquires - six months
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14575397
Minimum0
Maximum5
Zeros60486
Zeros (%)89.7%
Negative0
Negative (%)0.0%
Memory size527.2 KiB
2022-12-01T15:55:26.104530image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.47329129
Coefficient of variation (CV)3.2471931
Kurtosis15.143928
Mean0.14575397
Median Absolute Deviation (MAD)0
Skewness3.711972
Sum9833
Variance0.22400464
MonotonicityNot monotonic
2022-12-01T15:55:26.216215image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 60486
89.7%
1 4558
 
6.8%
2 2042
 
3.0%
3 320
 
0.5%
4 54
 
0.1%
5 3
 
< 0.1%
ValueCountFrequency (%)
0 60486
89.7%
1 4558
 
6.8%
2 2042
 
3.0%
3 320
 
0.5%
4 54
 
0.1%
5 3
 
< 0.1%
ValueCountFrequency (%)
5 3
 
< 0.1%
4 54
 
0.1%
3 320
 
0.5%
2 2042
 
3.0%
1 4558
 
6.8%
0 60486
89.7%

Open Account
Real number (ℝ)

Distinct36
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.266561
Minimum2
Maximum37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size527.2 KiB
2022-12-01T15:55:26.330259image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile7
Q110
median13
Q316
95-th percentile29
Maximum37
Range35
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.2250604
Coefficient of variation (CV)0.43633925
Kurtosis1.821184
Mean14.266561
Median Absolute Deviation (MAD)3
Skewness1.4651073
Sum962465
Variance38.751378
MonotonicityNot monotonic
2022-12-01T15:55:26.449469image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
12 8480
12.6%
13 7907
11.7%
11 7323
10.9%
14 6056
 
9.0%
10 5804
 
8.6%
9 4658
 
6.9%
15 3350
 
5.0%
8 3141
 
4.7%
16 2089
 
3.1%
7 1895
 
2.8%
Other values (26) 16760
24.8%
ValueCountFrequency (%)
2 6
 
< 0.1%
3 44
 
0.1%
4 197
 
0.3%
5 472
 
0.7%
6 1016
 
1.5%
7 1895
 
2.8%
8 3141
4.7%
9 4658
6.9%
10 5804
8.6%
11 7323
10.9%
ValueCountFrequency (%)
37 94
 
0.1%
36 152
 
0.2%
35 231
 
0.3%
34 346
0.5%
33 499
0.7%
32 513
0.8%
31 564
0.8%
30 631
0.9%
29 590
0.9%
28 668
1.0%

Public Record
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size527.2 KiB
0
62871 
1
 
4133
2
 
200
4
 
184
3
 
75

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters67463
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 62871
93.2%
1 4133
 
6.1%
2 200
 
0.3%
4 184
 
0.3%
3 75
 
0.1%

Length

2022-12-01T15:55:26.570345image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-01T15:55:26.682670image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0 62871
93.2%
1 4133
 
6.1%
2 200
 
0.3%
4 184
 
0.3%
3 75
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 62871
93.2%
1 4133
 
6.1%
2 200
 
0.3%
4 184
 
0.3%
3 75
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 67463
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 62871
93.2%
1 4133
 
6.1%
2 200
 
0.3%
4 184
 
0.3%
3 75
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 67463
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 62871
93.2%
1 4133
 
6.1%
2 200
 
0.3%
4 184
 
0.3%
3 75
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 67463
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 62871
93.2%
1 4133
 
6.1%
2 200
 
0.3%
4 184
 
0.3%
3 75
 
0.1%

Revolving Balance
Real number (ℝ)

Distinct20582
Distinct (%)30.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7699.3424
Minimum0
Maximum116933
Zeros7
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size527.2 KiB
2022-12-01T15:55:26.797309image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile528
Q12557
median5516
Q310184.5
95-th percentile22447.5
Maximum116933
Range116933
Interquartile range (IQR)7627.5

Descriptive statistics

Standard deviation7836.1482
Coefficient of variation (CV)1.0177685
Kurtosis16.903173
Mean7699.3424
Median Absolute Deviation (MAD)3469
Skewness2.9511352
Sum5.1942074 × 108
Variance61405218
MonotonicityNot monotonic
2022-12-01T15:55:26.941448image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1394 18
 
< 0.1%
3997 16
 
< 0.1%
311 16
 
< 0.1%
1202 15
 
< 0.1%
869 15
 
< 0.1%
360 15
 
< 0.1%
829 15
 
< 0.1%
1252 15
 
< 0.1%
574 14
 
< 0.1%
1428 14
 
< 0.1%
Other values (20572) 67310
99.8%
ValueCountFrequency (%)
0 7
< 0.1%
1 11
< 0.1%
2 6
< 0.1%
3 7
< 0.1%
4 10
< 0.1%
5 7
< 0.1%
6 6
< 0.1%
7 7
< 0.1%
8 6
< 0.1%
9 8
< 0.1%
ValueCountFrequency (%)
116933 1
< 0.1%
114621 1
< 0.1%
111223 1
< 0.1%
108050 1
< 0.1%
105820 1
< 0.1%
104159 1
< 0.1%
104133 1
< 0.1%
103901 1
< 0.1%
99484 1
< 0.1%
97895 1
< 0.1%

Revolving Utilities
Real number (ℝ)

Distinct67458
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.889443
Minimum0.00517236
Maximum100.88005
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size527.2 KiB
2022-12-01T15:55:27.088814image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.00517236
5-th percentile11.714745
Q138.658825
median54.082334
Q369.177117
95-th percentile88.513405
Maximum100.88005
Range100.87488
Interquartile range (IQR)30.518292

Descriptive statistics

Standard deviation22.53945
Coefficient of variation (CV)0.42616162
Kurtosis-0.54489853
Mean52.889443
Median Absolute Deviation (MAD)15.239263
Skewness-0.23724537
Sum3568080.5
Variance508.02682
MonotonicityNot monotonic
2022-12-01T15:55:27.230433image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39.61185859 2
 
< 0.1%
40.55837053 2
 
< 0.1%
91.31748448 2
 
< 0.1%
9.409246945 2
 
< 0.1%
66.65954626 2
 
< 0.1%
75.32411353 1
 
< 0.1%
70.09958645 1
 
< 0.1%
21.77597781 1
 
< 0.1%
92.26758399 1
 
< 0.1%
51.29358823 1
 
< 0.1%
Other values (67448) 67448
> 99.9%
ValueCountFrequency (%)
0.00517236 1
< 0.1%
0.021283828 1
< 0.1%
0.02999706 1
< 0.1%
0.035816294 1
< 0.1%
0.044888253 1
< 0.1%
0.051037999 1
< 0.1%
0.051458193 1
< 0.1%
0.054950741 1
< 0.1%
0.058213542 1
< 0.1%
0.05911439 1
< 0.1%
ValueCountFrequency (%)
100.8800498 1
< 0.1%
100.8668139 1
< 0.1%
100.8586132 1
< 0.1%
100.8553354 1
< 0.1%
100.8549743 1
< 0.1%
100.8356926 1
< 0.1%
100.8355207 1
< 0.1%
100.8265117 1
< 0.1%
100.8231983 1
< 0.1%
100.798556 1
< 0.1%

Total Accounts
Real number (ℝ)

Distinct69
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.627929
Minimum4
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size527.2 KiB
2022-12-01T15:55:27.358091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile6
Q113
median18
Q323
95-th percentile33
Maximum72
Range68
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.3192464
Coefficient of variation (CV)0.44660071
Kurtosis1.3267531
Mean18.627929
Median Absolute Deviation (MAD)5
Skewness0.73412152
Sum1256696
Variance69.209861
MonotonicityNot monotonic
2022-12-01T15:55:27.487745image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 3770
 
5.6%
17 3727
 
5.5%
19 3700
 
5.5%
20 3596
 
5.3%
16 3404
 
5.0%
21 3292
 
4.9%
22 3001
 
4.4%
15 2993
 
4.4%
23 2630
 
3.9%
14 2553
 
3.8%
Other values (59) 34797
51.6%
ValueCountFrequency (%)
4 1160
1.7%
5 1329
2.0%
6 1415
2.1%
7 1768
2.6%
8 1958
2.9%
9 2083
3.1%
10 2219
3.3%
11 2155
3.2%
12 2125
3.1%
13 2238
3.3%
ValueCountFrequency (%)
72 2
 
< 0.1%
71 2
 
< 0.1%
70 1
 
< 0.1%
69 1
 
< 0.1%
68 3
 
< 0.1%
67 2
 
< 0.1%
66 1
 
< 0.1%
65 8
< 0.1%
64 3
 
< 0.1%
63 3
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size527.2 KiB
w
36299 
f
31164 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters67463
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st roww
2nd rowf
3rd roww
4th roww
5th roww

Common Values

ValueCountFrequency (%)
w 36299
53.8%
f 31164
46.2%

Length

2022-12-01T15:55:27.611419image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-01T15:55:27.715138image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
w 36299
53.8%
f 31164
46.2%

Most occurring characters

ValueCountFrequency (%)
w 36299
53.8%
f 31164
46.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 67463
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 36299
53.8%
f 31164
46.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 67463
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 36299
53.8%
f 31164
46.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 67463
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
w 36299
53.8%
f 31164
46.2%

Total Received Interest
Real number (ℝ)

Distinct67451
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2068.9925
Minimum4.7367463
Maximum14301.368
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size527.2 KiB
2022-12-01T15:55:27.822901image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum4.7367463
5-th percentile160.09622
Q1570.90381
median1330.8428
Q32656.9568
95-th percentile6921.6826
Maximum14301.368
Range14296.632
Interquartile range (IQR)2086.053

Descriptive statistics

Standard deviation2221.9187
Coefficient of variation (CV)1.0739134
Kurtosis5.1874918
Mean2068.9925
Median Absolute Deviation (MAD)906.32473
Skewness2.1352431
Sum1.3958044 × 108
Variance4936922.9
MonotonicityNot monotonic
2022-12-01T15:55:27.956540image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
437.9250212 2
 
< 0.1%
2476.701276 2
 
< 0.1%
941.274347 2
 
< 0.1%
9061.050032 2
 
< 0.1%
607.2602052 2
 
< 0.1%
525.9471228 2
 
< 0.1%
3174.594809 2
 
< 0.1%
672.3328503 2
 
< 0.1%
453.6216698 2
 
< 0.1%
658.0427657 2
 
< 0.1%
Other values (67441) 67443
> 99.9%
ValueCountFrequency (%)
4.736746327 1
< 0.1%
4.740085405 1
< 0.1%
5.029317935 1
< 0.1%
5.037685745 1
< 0.1%
5.121524759 1
< 0.1%
5.167160376 1
< 0.1%
5.307849325 1
< 0.1%
5.397308646 1
< 0.1%
5.486680943 1
< 0.1%
5.521208352 1
< 0.1%
ValueCountFrequency (%)
14301.36831 1
< 0.1%
14290.59148 1
< 0.1%
14281.46799 1
< 0.1%
14258.3024 1
< 0.1%
14256.74704 1
< 0.1%
14255.15602 1
< 0.1%
14236.69012 1
< 0.1%
14227.81424 1
< 0.1%
14195.13756 1
< 0.1%
14172.13655 1
< 0.1%

Total Received Late Fee
Real number (ℝ)

Distinct67380
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1439686
Minimum3.06 × 10-6
Maximum42.618882
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size527.2 KiB
2022-12-01T15:55:28.097821image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum3.06 × 10-6
5-th percentile0.0042217567
Q10.021113871
median0.043397546
Q30.071883977
95-th percentile0.14803093
Maximum42.618882
Range42.618879
Interquartile range (IQR)0.050770106

Descriptive statistics

Standard deviation5.2443651
Coefficient of variation (CV)4.584361
Kurtosis25.992569
Mean1.1439686
Median Absolute Deviation (MAD)0.024643616
Skewness5.0845106
Sum77175.556
Variance27.503365
MonotonicityNot monotonic
2022-12-01T15:55:28.225483image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.037407587 2
 
< 0.1%
0.08816139 2
 
< 0.1%
0.058945258 2
 
< 0.1%
0.08077534 2
 
< 0.1%
0.030372569 2
 
< 0.1%
0.111829744 2
 
< 0.1%
9.98 × 10-52
 
< 0.1%
0.036133219 2
 
< 0.1%
0.083389349 2
 
< 0.1%
0.050149162 2
 
< 0.1%
Other values (67370) 67443
> 99.9%
ValueCountFrequency (%)
3.06 × 10-61
< 0.1%
3.84 × 10-61
< 0.1%
5.7 × 10-61
< 0.1%
1.27 × 10-51
< 0.1%
1.79 × 10-51
< 0.1%
1.9 × 10-51
< 0.1%
2 × 10-51
< 0.1%
2.07 × 10-51
< 0.1%
2.3 × 10-51
< 0.1%
2.38 × 10-52
< 0.1%
ValueCountFrequency (%)
42.6188823 1
< 0.1%
42.5951275 1
< 0.1%
42.58806301 1
< 0.1%
42.44903972 1
< 0.1%
42.41656912 1
< 0.1%
42.41545401 1
< 0.1%
42.38591859 1
< 0.1%
42.33250232 1
< 0.1%
42.30642544 1
< 0.1%
42.29851714 1
< 0.1%

Recoveries
Real number (ℝ)

Distinct67387
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.691578
Minimum3.56 × 10-5
Maximum4354.4674
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size527.2 KiB
2022-12-01T15:55:28.361998image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum3.56 × 10-5
5-th percentile0.33131175
Q11.6298176
median3.3445241
Q35.4537268
95-th percentile9.1838589
Maximum4354.4674
Range4354.4674
Interquartile range (IQR)3.8239093

Descriptive statistics

Standard deviation357.02635
Coefficient of variation (CV)5.9811846
Kurtosis58.183685
Mean59.691578
Median Absolute Deviation (MAD)1.8681336
Skewness7.3717873
Sum4026972.9
Variance127467.81
MonotonicityNot monotonic
2022-12-01T15:55:28.490263image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.816960301 2
 
< 0.1%
4.764209111 2
 
< 0.1%
5.151216155 2
 
< 0.1%
6.983539658 2
 
< 0.1%
3.225564581 2
 
< 0.1%
3.772796302 2
 
< 0.1%
3.950778781 2
 
< 0.1%
2.641522989 2
 
< 0.1%
2.753067871 2
 
< 0.1%
1.013758103 2
 
< 0.1%
Other values (67377) 67443
> 99.9%
ValueCountFrequency (%)
3.56 × 10-51
< 0.1%
9.02 × 10-51
< 0.1%
0.000220811 1
< 0.1%
0.000350407 1
< 0.1%
0.000372444 1
< 0.1%
0.00042457 1
< 0.1%
0.000595908 1
< 0.1%
0.000696606 1
< 0.1%
0.000865389 1
< 0.1%
0.000882372 1
< 0.1%
ValueCountFrequency (%)
4354.467419 1
< 0.1%
4339.261318 1
< 0.1%
4330.782063 1
< 0.1%
4325.079801 1
< 0.1%
4313.548899 1
< 0.1%
4299.375307 1
< 0.1%
4262.898182 1
< 0.1%
4254.856665 1
< 0.1%
4241.493689 1
< 0.1%
4220.932555 1
< 0.1%

Collection Recovery Fee
Real number (ℝ)

Distinct67313
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1251409
Minimum3.62 × 10-5
Maximum166.833
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size527.2 KiB
2022-12-01T15:55:28.629433image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum3.62 × 10-5
5-th percentile0.13024329
Q10.47625936
median0.78014063
Q31.0705655
95-th percentile1.426481
Maximum166.833
Range166.83296
Interquartile range (IQR)0.59430618

Descriptive statistics

Standard deviation3.4898845
Coefficient of variation (CV)3.101731
Kurtosis173.3263
Mean1.1251409
Median Absolute Deviation (MAD)0.29662508
Skewness11.102131
Sum75905.383
Variance12.179294
MonotonicityNot monotonic
2022-12-01T15:55:28.761634image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.125373051 3
 
< 0.1%
0.53373764 2
 
< 0.1%
0.792153159 2
 
< 0.1%
1.139062259 2
 
< 0.1%
0.345641963 2
 
< 0.1%
1.17911419 2
 
< 0.1%
0.577143464 2
 
< 0.1%
0.564290849 2
 
< 0.1%
1.081363446 2
 
< 0.1%
1.196215319 2
 
< 0.1%
Other values (67303) 67442
> 99.9%
ValueCountFrequency (%)
3.62 × 10-51
< 0.1%
4.5 × 10-51
< 0.1%
7.34 × 10-51
< 0.1%
8.09 × 10-51
< 0.1%
0.000144092 1
< 0.1%
0.000184412 1
< 0.1%
0.000236446 1
< 0.1%
0.000261 1
< 0.1%
0.000359239 1
< 0.1%
0.000390586 1
< 0.1%
ValueCountFrequency (%)
166.833 1
< 0.1%
54.22278838 1
< 0.1%
53.46508418 1
< 0.1%
51.42711665 1
< 0.1%
51.04841185 1
< 0.1%
50.84705334 1
< 0.1%
50.66485507 1
< 0.1%
49.94395918 1
< 0.1%
49.85959833 1
< 0.1%
49.52562248 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size527.2 KiB
0
66026 
1
 
1437

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters67463
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 66026
97.9%
1 1437
 
2.1%

Length

2022-12-01T15:55:28.890277image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-01T15:55:29.603845image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0 66026
97.9%
1 1437
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 66026
97.9%
1 1437
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 67463
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 66026
97.9%
1 1437
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 67463
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 66026
97.9%
1 1437
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 67463
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 66026
97.9%
1 1437
 
2.1%

Application Type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size527.2 KiB
INDIVIDUAL
67340 
JOINT
 
123

Length

Max length10
Median length10
Mean length9.9908839
Min length5

Characters and Unicode

Total characters674015
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowINDIVIDUAL
2nd rowINDIVIDUAL
3rd rowINDIVIDUAL
4th rowINDIVIDUAL
5th rowINDIVIDUAL

Common Values

ValueCountFrequency (%)
INDIVIDUAL 67340
99.8%
JOINT 123
 
0.2%

Length

2022-12-01T15:55:29.732500image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-01T15:55:29.865153image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
individual 67340
99.8%
joint 123
 
0.2%

Most occurring characters

ValueCountFrequency (%)
I 202143
30.0%
D 134680
20.0%
N 67463
 
10.0%
V 67340
 
10.0%
U 67340
 
10.0%
A 67340
 
10.0%
L 67340
 
10.0%
J 123
 
< 0.1%
O 123
 
< 0.1%
T 123
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 674015
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 202143
30.0%
D 134680
20.0%
N 67463
 
10.0%
V 67340
 
10.0%
U 67340
 
10.0%
A 67340
 
10.0%
L 67340
 
10.0%
J 123
 
< 0.1%
O 123
 
< 0.1%
T 123
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 674015
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 202143
30.0%
D 134680
20.0%
N 67463
 
10.0%
V 67340
 
10.0%
U 67340
 
10.0%
A 67340
 
10.0%
L 67340
 
10.0%
J 123
 
< 0.1%
O 123
 
< 0.1%
T 123
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 674015
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 202143
30.0%
D 134680
20.0%
N 67463
 
10.0%
V 67340
 
10.0%
U 67340
 
10.0%
A 67340
 
10.0%
L 67340
 
10.0%
J 123
 
< 0.1%
O 123
 
< 0.1%
T 123
 
< 0.1%

Last week Pay
Real number (ℝ)

Distinct162
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.16326
Minimum0
Maximum161
Zeros131
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size527.2 KiB
2022-12-01T15:55:29.975905image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q135
median68
Q3105
95-th percentile147
Maximum161
Range161
Interquartile range (IQR)70

Descriptive statistics

Standard deviation43.315845
Coefficient of variation (CV)0.6086827
Kurtosis-0.98490299
Mean71.16326
Median Absolute Deviation (MAD)35
Skewness0.26198885
Sum4800887
Variance1876.2625
MonotonicityNot monotonic
2022-12-01T15:55:30.110999image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 743
 
1.1%
13 729
 
1.1%
12 714
 
1.1%
11 713
 
1.1%
10 710
 
1.1%
15 695
 
1.0%
16 661
 
1.0%
9 638
 
0.9%
17 624
 
0.9%
18 596
 
0.9%
Other values (152) 60640
89.9%
ValueCountFrequency (%)
0 131
 
0.2%
1 141
 
0.2%
2 203
 
0.3%
3 252
 
0.4%
4 289
0.4%
5 369
0.5%
6 402
0.6%
7 485
0.7%
8 595
0.9%
9 638
0.9%
ValueCountFrequency (%)
161 163
0.2%
160 174
0.3%
159 204
0.3%
158 213
0.3%
157 239
0.4%
156 232
0.3%
155 239
0.4%
154 238
0.4%
153 261
0.4%
152 253
0.4%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size527.2 KiB
0
67463 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters67463
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 67463
100.0%

Length

2022-12-01T15:55:30.236212image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-01T15:55:30.332476image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0 67463
100.0%

Most occurring characters

ValueCountFrequency (%)
0 67463
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 67463
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 67463
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 67463
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 67463
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 67463
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 67463
100.0%

Total Collection Amount
Real number (ℝ)

Distinct2193
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean146.46799
Minimum1
Maximum16421
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size527.2 KiB
2022-12-01T15:55:30.438273image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q124
median36
Q346
95-th percentile496
Maximum16421
Range16420
Interquartile range (IQR)22

Descriptive statistics

Standard deviation744.38223
Coefficient of variation (CV)5.0822179
Kurtosis207.01677
Mean146.46799
Median Absolute Deviation (MAD)11
Skewness12.910972
Sum9881170
Variance554104.91
MonotonicityNot monotonic
2022-12-01T15:55:30.575414image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39 1665
 
2.5%
37 1656
 
2.5%
36 1637
 
2.4%
41 1623
 
2.4%
40 1622
 
2.4%
35 1616
 
2.4%
34 1597
 
2.4%
33 1590
 
2.4%
42 1566
 
2.3%
32 1565
 
2.3%
Other values (2183) 51326
76.1%
ValueCountFrequency (%)
1 310
0.5%
2 320
0.5%
3 344
0.5%
4 390
0.6%
5 413
0.6%
6 452
0.7%
7 477
0.7%
8 533
0.8%
9 563
0.8%
10 618
0.9%
ValueCountFrequency (%)
16421 1
< 0.1%
16385 1
< 0.1%
16086 1
< 0.1%
16013 1
< 0.1%
15956 1
< 0.1%
15916 1
< 0.1%
15895 1
< 0.1%
15663 1
< 0.1%
15460 1
< 0.1%
15459 1
< 0.1%

Total Current Balance
Real number (ℝ)

Distinct60901
Distinct (%)90.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean159573.93
Minimum617
Maximum1177412
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size527.2 KiB
2022-12-01T15:55:30.716586image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum617
5-th percentile18215.3
Q150379
median118369
Q3228375
95-th percentile432485.9
Maximum1177412
Range1176795
Interquartile range (IQR)177996

Descriptive statistics

Standard deviation139033.25
Coefficient of variation (CV)0.87127792
Kurtosis3.1257696
Mean159573.93
Median Absolute Deviation (MAD)78486
Skewness1.5115779
Sum1.0765336 × 1010
Variance1.9330243 × 1010
MonotonicityNot monotonic
2022-12-01T15:55:30.860788image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51737 5
 
< 0.1%
36268 5
 
< 0.1%
69865 4
 
< 0.1%
51723 4
 
< 0.1%
44225 4
 
< 0.1%
82583 4
 
< 0.1%
85459 4
 
< 0.1%
34633 4
 
< 0.1%
40640 4
 
< 0.1%
40288 4
 
< 0.1%
Other values (60891) 67421
99.9%
ValueCountFrequency (%)
617 1
< 0.1%
623 1
< 0.1%
628 1
< 0.1%
630 1
< 0.1%
667 1
< 0.1%
681 1
< 0.1%
691 1
< 0.1%
707 1
< 0.1%
710 1
< 0.1%
798 1
< 0.1%
ValueCountFrequency (%)
1177412 1
< 0.1%
1165601 1
< 0.1%
1157944 1
< 0.1%
1150619 1
< 0.1%
1145991 1
< 0.1%
1140709 1
< 0.1%
1128432 1
< 0.1%
1114351 1
< 0.1%
1091714 1
< 0.1%
1071342 1
< 0.1%
Distinct37708
Distinct (%)55.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23123.006
Minimum1000
Maximum201169
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size527.2 KiB
2022-12-01T15:55:31.008817image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile2723.2
Q18155.5
median16733
Q332146.5
95-th percentile63298.9
Maximum201169
Range200169
Interquartile range (IQR)23991

Descriptive statistics

Standard deviation20916.7
Coefficient of variation (CV)0.90458396
Kurtosis5.9800859
Mean23123.006
Median Absolute Deviation (MAD)10394
Skewness1.9771503
Sum1.5599473 × 109
Variance4.3750834 × 108
MonotonicityNot monotonic
2022-12-01T15:55:31.148293image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5310 10
 
< 0.1%
7026 10
 
< 0.1%
6083 9
 
< 0.1%
4754 9
 
< 0.1%
5413 9
 
< 0.1%
5642 9
 
< 0.1%
7058 9
 
< 0.1%
12364 9
 
< 0.1%
4895 8
 
< 0.1%
11614 8
 
< 0.1%
Other values (37698) 67373
99.9%
ValueCountFrequency (%)
1000 2
 
< 0.1%
1001 5
< 0.1%
1003 1
 
< 0.1%
1005 3
< 0.1%
1007 1
 
< 0.1%
1008 5
< 0.1%
1009 1
 
< 0.1%
1010 3
< 0.1%
1011 4
< 0.1%
1013 1
 
< 0.1%
ValueCountFrequency (%)
201169 1
< 0.1%
197112 1
< 0.1%
193312 1
< 0.1%
192276 1
< 0.1%
190060 1
< 0.1%
189087 1
< 0.1%
188063 1
< 0.1%
185719 1
< 0.1%
185594 1
< 0.1%
184987 1
< 0.1%

Loan Status
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size527.2 KiB
0
61222 
1
6241 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters67463
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 61222
90.7%
1 6241
 
9.3%

Length

2022-12-01T15:55:31.279899image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-01T15:55:31.382625image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0 61222
90.7%
1 6241
 
9.3%

Most occurring characters

ValueCountFrequency (%)
0 61222
90.7%
1 6241
 
9.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 67463
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 61222
90.7%
1 6241
 
9.3%

Most occurring scripts

ValueCountFrequency (%)
Common 67463
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 61222
90.7%
1 6241
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 67463
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 61222
90.7%
1 6241
 
9.3%

Interactions

2022-12-01T15:55:16.632137image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:07.849591image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:11.675581image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:14.968598image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:18.395779image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:21.514764image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:25.155662image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:28.466959image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:31.768489image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:34.925082image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:37.737510image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:41.616426image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:44.677676image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:47.667150image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:51.732520image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:56.567761image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:59.825894image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:55:03.021573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:55:06.181876image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:55:09.786977image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:55:13.309798image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:55:16.788718image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:08.108514image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:11.818244image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:15.108230image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:18.549904image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:21.665944image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:25.329198image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:28.629364image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:31.900144image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:35.056692image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:37.876353image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:41.803937image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:44.831252image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:47.810661image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:51.920313image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:56.724851image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:59.971471image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:55:03.184172image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:55:06.349593image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:55:09.938570image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:55:13.455409image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:55:16.984208image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:08.326930image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:12.034940image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:15.275812image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:18.705488image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:21.800580image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:25.479832image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:28.797363image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:32.039103image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:35.194324image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:38.043531image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:41.978445image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:44.975883image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:47.986192image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:52.077610image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:56.881940image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:55:00.153392image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:55:03.347702image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:55:06.524127image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:55:10.085145image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:55:13.631937image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:55:17.184813image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:08.532381image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:12.207463image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:15.586221image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:18.851066image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:21.932937image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:25.628398image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:28.963469image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:32.182585image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:35.333951image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:38.203542image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:42.139055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:45.120100image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:48.185658image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:52.284464image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-12-01T15:54:57.053255image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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Correlations

2022-12-01T15:55:31.523752image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-12-01T15:55:31.929987image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-01T15:55:32.351569image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-01T15:55:32.801772image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-01T15:55:33.171823image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-12-01T15:55:33.399218image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-01T15:55:20.713487image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-01T15:55:21.707270image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IDLoan AmountFunded AmountFunded Amount InvestorTermBatch EnrolledInterest RateGradeSub GradeEmployment DurationHome OwnershipVerification StatusPayment PlanLoan TitleDebit to IncomeDelinquency - two yearsInquires - six monthsOpen AccountPublic RecordRevolving BalanceRevolving UtilitiesTotal AccountsInitial List StatusTotal Received InterestTotal Received Late FeeRecoveriesCollection Recovery FeeCollection 12 months MedicalApplication TypeLast week PayAccounts DelinquentTotal Collection AmountTotal Current BalanceTotal Revolving Credit LimitLoan Status
065087372100003223612329.36286059BAT252292211.135007BC4MORTGAGE176346.62670Not VerifiednDebt Consolidation16.284758101302424674.9325517w2929.6463150.1020552.4982910.7937240INDIVIDUAL4903131130166190
1145015336091194012191.99692059BAT158659912.237563CD3RENT39833.92100Source VerifiednDebt consolidation15.4124090012081278.29718613f772.7693850.0361812.3772150.9748210INDIVIDUAL109053182610208850
2196910128276931121603.22455059BAT213639112.545884FD4MORTGAGE91506.69105Source VerifiednDebt Consolidation28.1376190014018432.07304020w863.32439618.7786604.3162771.0200750INDIVIDUAL6603489801261550
3665143011170695417877.15585059BAT242873116.731201CC3MORTGAGE108286.57590Source VerifiednDebt consolidation18.04373010701381967.46795112w288.1731960.0441310.1070200.7499710INDIVIDUAL390409189602140
414354669168901322613539.92667059BAT534161915.008300CD4MORTGAGE44234.82545Source VerifiednCredit card refinancing17.20988613131154485.25076122w129.23955319.3066461294.8187510.3689530INDIVIDUAL180430126029225790
55050904634631302038635.93161336BAT469457217.246986BG5RENT98957.47561Not VerifiednCredit card refinancing7.91433332160227751.56447620w464.8181240.0885845.0435750.5816880INDIVIDUAL3204251252274800
632737431308441977315777.51183059BAT480802210.731432CC5RENT102391.82430VerifiednHome improvement15.083911001101450146.80880437w525.7381090.0835283.1679370.5530760INDIVIDUAL710338842069310680
76315165020744106097645.01480258BAT255838813.993688AA5OWN61723.52014Not VerifiednDebt consolidation29.829715001401306723.93662433w1350.2452120.0449650.0984480.0475890INDIVIDUAL87048184909433030
8427966292991123813429.45661059BAT534161911.178457GC2MORTGAGE63205.09072VerifiednCredit card refinancing26.244710006054915.94738617w4140.1989780.0171060.5302140.2169850INDIVIDUAL1440266812674820
944310341923289627004.09748158BAT20789745.520413CB5RENT42015.46586Source VerifiednCredit card refinancing10.04854910110136135.07334530f2149.6669630.0083382.9122150.8868640INDIVIDUAL903571650148710
IDLoan AmountFunded AmountFunded Amount InvestorTermBatch EnrolledInterest RateGradeSub GradeEmployment DurationHome OwnershipVerification StatusPayment PlanLoan TitleDebit to IncomeDelinquency - two yearsInquires - six monthsOpen AccountPublic RecordRevolving BalanceRevolving UtilitiesTotal AccountsInitial List StatusTotal Received InterestTotal Received Late FeeRecoveriesCollection Recovery FeeCollection 12 months MedicalApplication TypeLast week PayAccounts DelinquentTotal Collection AmountTotal Current BalanceTotal Revolving Credit LimitLoan Status
674531466955431161160008386.74692959BAT48080226.524646AC2MORTGAGE81220.63670Source VerifiednDebt consolidation34.387740009050551.61331422f1057.8260020.0068611.1090810.9425390INDIVIDUAL90399204397050
6745437933019712258968740.58984158BAT213639114.729811BA3MORTGAGE39889.60578Not VerifiednCredit card refinancing29.4753350011026325.11376219f2934.5402770.0287370.2556951.0830930INDIVIDUAL36018311173467240
674556096151851271695613917.48522059BAT252292219.388683CF3MORTGAGE99748.53668Source VerifiednCredit card refinancing33.62229400100129649.16080018f265.4799680.0931461.8051521.0854860INDIVIDUAL3603943981162190
674563902239011703197369972.20269659BAT554720111.430757ED4MORTGAGE50548.01172Source VerifiednCredit card refinancing32.6376180014077877.36071824f380.9073940.0678631.2332400.7780510INDIVIDUAL290317445397710
674577273094114401767222965.76290059BAT255838815.025260CB1RENT76128.78634VerifiednDebt consolidation21.9296980080526012.0806627w2258.0387120.0107220.0610960.3255640INDIVIDUAL151038859647214680
674581616494513601684813175.28583059BAT31936899.408858CA4MORTGAGE83961.15003VerifiednCredit card refinancing28.10512710130411297.77938919w1978.9459600.023478564.6148520.8652300INDIVIDUAL69048181775343011
674593518271483231104615637.46301059BAT17805179.972104CB3RENT65491.12817Source VerifiednCredit card refinancing17.69427900120973715.69070314w3100.8031250.0270952.0154941.4033680INDIVIDUAL140372269287140
6746016435904158973292112329.45775059BAT176198119.650943AF3MORTGAGE34813.96985VerifiednLending loan10.295774007121951.5000909w2691.9955320.0282125.6730921.6070930INDIVIDUAL137017176857423300
67461530032516567497521353.68465059BAT233341213.169095DE3OWN96938.83564Not VerifiednDebt consolidation7.61462400140117268.48188215f3659.3342020.0745081.1574540.2076080INDIVIDUAL73061361339390750
6746265443173153532987514207.44860059BAT193036516.034631BD1MORTGAGE105123.15580VerifiednDebt consolidation16.05211200300876281.69232816f1324.2559220.0006711.8564800.3663860INDIVIDUAL54047196960660600